Study on Time-sharing Reservation Recommendation System of Scenic Spots Based on Relationship Graph

Caihone Li, Linjie Luo, Xiaojia Huane, Haoyin Lv, Mian Qin
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Abstract

With the continuous development of the Internet, the content and information in the network are increasing explosively. Nowadays, with the rapid increase of data volume, many Internet companies need to consider how to promote their products to potential users and realize personalized recommendation. To solve this problem, people need to find relationships between data to build a relationship graph. Then, the potential relationship between each node and other nodes can be found according to the relationship graph, and personalized recommendation can be finally realized. Based on the reservation records of scenic spots in Gansu Province from 2020 to 2021, this paper builds a relationship graph between tourists and scenic spots. Neo4j graph database is used to store the relationship graph and display it intuitively. Then the relationship graph is used as the input of the recommendation system model in this paper to obtain the embedding representation of each type of tourists. Then, K-means is used to cluster tourists to obtain the reserved scenic spots of each category and calculate the popularity of scenic spots, so as to obtain TOP-K scenic spot recommendation and realize personalized recommendation.
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基于关系图的景区分时预订推荐系统研究
随着互联网的不断发展,网络中的内容和信息呈爆炸式增长。如今,随着数据量的快速增长,许多互联网公司需要考虑如何向潜在用户推广自己的产品,实现个性化推荐。为了解决这个问题,人们需要找到数据之间的关系来构建关系图。然后,根据关系图找到每个节点与其他节点之间的潜在关系,最终实现个性化推荐。基于2020 - 2021年甘肃省旅游景区的预订记录,构建游客与旅游景区的关系图。使用Neo4j图形数据库存储关系图并直观显示。然后将关系图作为本文推荐系统模型的输入,得到各类型游客的嵌入表示。然后利用K-means对游客进行聚类,得到每个类别的预留景点,并计算景点的受欢迎程度,从而得到TOP-K的景点推荐,实现个性化推荐。
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